Xu et al. proposed G-TAD [1]} to capture the relations between different snippets of input video. They constructed a graph where the nodes are temporal segments of the video and the edges are either temporal or semantic. The temporal edges are pre-defined according to the snippets’ temporal order but the semantic edges are dynamically updated between the nodes according to their feature distance. Temporal and semantic context of the snippets are aggregated using graph convolutions. All possible pairs of start and end with duration within a specific range are considered to generate the proposals. To evaluate each proposal, the temporal and semantic features of the corresponding sub-graph are extracted. Chang et al. in ATAG [2]} also designed an adaptive GCN similar to G-TAD [1]} to capture local temporal context where the graph nodes are the snippets and the edges model the relation between snippets. The temporal context is then captured through graph convolutions where the feature of each snippet is influenced and updated by the features of other snippets. VSGN [4]} builds a graph on video snippets similar to G-TAD [1]}, but also exploits correlations between cross-scale snippets. They propose a cross-scale graph pyramid network which aggregates features from cross scales, and progressively enhances the features of original and magnified scales at multiple network levels.
